Combining the intranetwork and transnetwork findings, these data provide strongest support for the transneuronal spread model, which predicts that the strength of any node’s functional connectivity to an epicenter will determine that node’s ultimate vulnerability to a neurodegeneration once the disease has taken hold. In contrast to the intranetwork analysis, we found no consistent evidence for the nodal stress model’s predictions at

the selleckchem transnetwork level, perhaps because across a broader brain network space a node’s centrality need not determine its susceptibility to every disease process. As seen at the intra-network level, at the transnetwork level we found no consistent evidence supporting predictions derived from the trophic failure or shared vulnerability models. Several important limitations of this study should be noted. The AD group used to define the anatomical pattern studied here included patients with early age-of-onset AD, which features a more distributed cortical pattern when compared to the hippocampal-predominant pattern seen in late age-of-onset patients (Kim et al., 2005). This factor could account, at least in part, for the identification of the angular gyrus as the lone epicenter within the AD pattern. The present analyses used regional functional connectivity approaches selleck inhibitor in a healthy older control group to predict neurodegeneration severity

in patients. Although the human connectome evolves with aging (Zuo et al., 2010), we chose healthy older subjects to capture the connectome upon which neurodegeneration is most often superimposed. Although we cannot exclude preclinical neurodegeneration in our control sample, each subject was screened with a battery of neuropsychological tests and found to perform within normal limits mafosfamide for age. The ideal approach for predicting neurodegeneration from connectivity data would be to follow individuals from health to disease, exploring connectivity-vulnerability

interactions within single subjects. Although this approach may prove challenging for the FTD syndromes studied here, future longitudinal analyses of this type should become feasible for AD through large, ongoing, collaborative longitudinal studies. Although we used the same five group-level atrophy maps to identify the epicenter “candidate pool” for each disease and to assess connectivity-vulnerability relationships, several key design elements prevented circularity. First, atrophy severity served as the major outcome variable but was not involved in epicenter identification. Second, the healthy network matrices used for calculating graph metrics were epicenter-independent, composed of every region within each binary atrophy map. Third, the transnetwork graphs and analyses (Figures 5 and 6) spanned regions from all five binary atrophy maps.